Source: UNIVERSITY OF MARYLAND submitted to
AUTOMATED WHOLE-SURFACE IMAGING TECHNOLOGY FOR ON-LINE SAFETY AND QUALITY INSPECTION OF PRODUCE
Sponsoring Institution
Agricultural Research Service/USDA
Project Status
NEW
Funding Source
USDA COOPERATIVE AGREEMENT
Reporting Frequency
Annual
Accession No.
0430485
Grant No.
(N/A)
Project No.
8042-42000-020-001S
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
May 1, 2016
Project End Date
Dec 31, 2019
Grant Year
(N/A)
Project Director
KIM M S
Recipient Organization
UNIVERSITY OF MARYLAND
(N/A)
BALTIMORE,MD 21201
Performing Department
MECHANICAL ENGINEERING
Non Technical Summary
(N/A)
Animal Health Component
(N/A)
Research Effort Categories
Basic
30%
Applied
30%
Developmental
40%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
7121430202050%
7121460202050%
Knowledge Area
712 - Protect Food from Contamination by Pathogenic Microorganisms, Parasites, and Naturally Occurring Toxins;

Subject Of Investigation
1460 - Tomato; 1430 - Greens and leafy vegetables;

Field Of Science
2020 - Engineering;
Goals / Objectives
The objective is to develop automated whole-surface image segmentation methods for fruit inspection. In addition, the multitask line-scan imaging technologies coupled with the proposed development of whole-surface imaging methods will be integrated to develop automated online whole-surface safety and quality inspection prototypes for relatively round-shaped fruits.
Project Methods
For online safety and quality screening, the whole surfaces of produce items must be evaluated. Automated online produce inspection prototypes will be jointly developed to allow for inspection of whole surfaces of round fruits. The instrumentation and sensing platform will incorporate multitask line-scan imaging methods for simultaneous reflectance and fluorescence imaging, with the proposed development of automated whole-surface image segmentation methods. This will allow screening of nearly 100% of the surfaces of produce for fecal contaminants and defects during online processing.